Search

Advances in automotive industry as well as computing technology are making autonomous vehicle (AV) an appealing means of transportation. Vehicles are beyond the traditional source of commute, and leveled up to smart devices with computing capability. As one of the compelling features of AVs, the autonomous valet parking (AVP) allows for navigating and parking the car automatically without human interventions. Within this realm, long-range AVP (LAVP) extends auto-parking to a much larger scale compared to its short-range counterparts. It is worth noting that AV mobility is a pivotal concern with LAVP, involving dynamic patterns related to spatial-temporal features, such as varied parking and drop-off (or pick-up) demands with diverse customer journey planning. We herein target such critical decision-making on where to park and where to drop/pick-up upon customer requirements during their journeys. Concerning in practice that car parks are equipped with limited parking space, we thus introduce parking reservations and enable accurate estimations on future parking states. An efficient LAVP service framework enhanced with parking reservations is then proposed. Benefited from the intelligent predictions, parking load can be accurately predicted and greatly alleviated at individual car parks, thereby avoiding overcrowding effectively. Results show that significant performance gains can be achieved under the proposed scheme by comparing to other benchmarks, with respect to greatly reduced waiting duration for available parking space, as well as enhanced customer experiences in terms of reduced traveling period, etc. In particular, the number of parked vehicles across the network can be effectively balanced.

Compared with traditional internal combustion engine vehicles, Electric Vehicles (EVs) have the advantage of eliminating harmful gases in the environment, with great development potential in recent years. However, because the battery capacity of EVs is limited at the current stage, where to charge (to select charging station) and when/whether to charge (order the charging priority of EVs) still limit the large-scale popularity of EVs. In this paper, we develop an Urgency First Charging (UFC) charging scheduling policy, which takes the remaining parking time and charging time of EVs as the standard of charging priority. With this, the CS benefits to the shortest trip duration (summation of travelling time through CS, and charging service time at CS) is selected as optimal solution. We have conducted simulations through Helsinki’s traffic scenarios. The results have shown that our proposed CS-Selection scheme effectively improves the charging comfort (in terms of waiting time and trip time) and charging efficiency (in terms of not-fully charged service due to limited parking duration).

Electric Vehicles (EVs) are environmental friendly comparing with traditional internal combustion vehicles (ICVs), and have great application potential to achieve green transportation. However, due to the battery technology under development, the charging time of EVs is still longer than refuelling time of ICVs. Importantly, CS-Selection scheme (which/where to charge) and charging scheduling (when/whether to charge) are key solutions, for coping with long charging time and uneven distribution of Charging Stations (CSs) in urban city. In this paper, we propose an Urgency First Charging (UFC) scheduling policy, which orders EVs via their charging urgency (calculated by their charging demand and remaining parking duration). With the underlying UFC policy, we further propose a reservation-based CS-Selection scheme that selects the optimal CS with the minimum trip duration (summation of travelling time through CS, and the charging time spent at CS), where the EVs would further report their reservations to help anticipate the service congestion status of CSs in future. We have conducted simulations through Helsinki’s city traffic scenarios. The simulation results show that our proposed CS-Selection scheme has advantages in improving users quality of experience, which shortens the overall trip duration of EVs and fully charges more EVs before departure deadline.